We start with some discussion of the "Global Brain."  This is a metaphor or an analogy - what does it mean to say that the global computer network is a "brain".  It means that the brain and the global computer network have organizational traits in common, that they are organized in similar ways.

•  Peter Russell's book The Global Brain introduced this term.  Part of a long historical trend, including steps such as the invention of language, writing, printing telecommunications and now computers and the internet
•    argues that we are developing into a global community, with information as the key resource - a point that many have made
•   the development of the societal nervous system is analogous to the development  of the human nervous system - increasing number of connections between cells resulting in a "brain"

Theories of Chaos and Complexity

• General Systems Theory Developed in the 1940-1960 period, limited by the lack of computers to do simulations
• Chaos theory is the study of deterministic, rule-based systems that nevertheless appear to be acting completely unpredictably, even at random - An example is the "logistic equation" which can be modeled in a spreadsheet program.  It is possible to produce the graph at right with the logistic equation, Xn+1 = AXn(1 - Xn)
• This is most striking in the physical sciences, where measurement is precise and theory would lead us to expect things to be predictable.  Meteorology is a common example.
• The What is Chaos lecture provides a good outline
• contrasts chaos theory to deterministic science
• refers to Newton's Three Laws of Motion as an example of deterministic science
• chaos theory deals with situations which are highly sensitive to initial conditions
• no measurements can be perfectly precise, which means that systems which are highly dependent on initial conditions are unstable
• Chaos is not just due to errors in measurement,  but can be shown in pure mathematics, generating "strange attractors" out of fairly simple formulas.
• Complexity is not the same as chaos, it refers to systems where a large number of variables interact with each other.  Complex systems may or may not lead to chaotic behavior.
• We are interested in studying complexity  - in general - because we may find similar patterns emerging in diverse systems.  Otherwise, we can study each complex system independently.  Thus, we believe that systems as different as the brain, families, the climate, auto traffic, earthquakes and financial markets may have things in common.

• fixed point - constant, not changing
• limit cycle -  changing within a fixed pattern, periodic, e.g., motion of the planets.  These can be studied with mathematics, e.g., Newton's laws, calculus...
• Strange Attractors are patterns which have irregular, nonlinear shapes and which emerge out of repeated iterations of a system.  They must be studied with computer simulation.  They often make beautiful graphics.
• There is evidence that strange attractors exist in the brain, e.g., in the olfactory lobes of rabbits

• evolution requires a large number of competing entities, which can survive or not survive depending on circumstances in their environment
• these entities must also have the ability to mutate or combine to form new entities

• entities which maintain their structure despite environmental pressure to change
• if part of the structure is destroyed, the other parts act to replace or compensate for it

the ability to create new components which serve to strengthen and maintain the existing system

Why look at metaphysics?

• previous efforts at AI have oversimplified "mind"
• the metaphysicians have been examining how we think for centuries, and they have reached some interesting conclusions
• many of the metaphysicians have anticipated ideas that scientists have discovered only much later:  atoms, quantum indeterminacy, digital logic, for example
• it is likely that our brains evolved as they did because that is the best way to think, other intelligent beings would probably think in much the same way - including ones we might create
• we need a vision of the big picture, as well as detailed computer code

• an interesting thinker because he was both a philosopher and a scientist
• he anticipated many key findings simply by thinking about how he and others thought - his assumption was that the rest of the world would function much as his brain did
• spontaneity rather than determinism
• random swerving of microscopic particles
• the brain has no central cell
• small integers as fundamental units of organization in the universe - numbers as "archetypes"

Numbers as Archetypes

• many philosophers have seen numbers  - particularly the small integers - as "archetypal", as a fundamental organizing principle for reality.  The Pythagoreans were especially strong on this idea.
• Peirce was particularly taken with first, second and third as archetypes.  But he also thought about zero, and about combinations of the first three, e.g., firstness of second, secondness of third, etc.
• Carl Jung also thought numbers were archetypal, but he placed much more emphasis on the number four which he saw as the archetype of synergy, or self-sustaining order.  Ben and I agree with Jung on this.  Higher numbers can also be seen as archetypal
• this may seem like foolish speculation, but it is remarkable that the same imagery occurs both in classical creation myths and in the latest astronomical theories of the origins of the universe

• the original state of the universe - some physicists believe that a "zero-point field" or quantum vacuum underlies all apparent mass and energy in the universe.
• scientific astronomy has reached the same conclusion
• not the same as the integer "0" - this is something before arithmetic existed, before order of any kind existed
• also found in many religious traditions, e.g., the Buddhist concept of the "Formless Void" - meditation seeks to bring the worshipper into this state
• perhaps this concept is captured by The Really Big Button That Doesn't Do Anything.
• complex systems theory also shows that order can emerge out of chaos

• the conception of being or existing independent of anything else
• the first step in the emergence of order out of chaos
• in human psychology, firstness comprises feelings that are immediately present, such as pain, blue, cheerfulness, the feeling that arises when we contemplate a consistent theory, when we contemplate God.
• in physics, it is the quantum indeterminacy of matter, behavior which does not follow any law
• in philosophy, it is usually equated with idealism, the position that ideas just emerge on their own rather than being determined by physical realities

• for Peirce, secondness is the conception of being relative to, the conception of reaction with, something else
• in psychology, sensations of reaction
• in physics, laws of relationship between variables
• in mathematics, a vector - a line with an arrow at the end, having both position and direction
• in complex systems theory, the movement from one state to another

• for Peirce, this is habit, abstract thought, the process whereby two things are understood to be in a relationshp
• these feelings and thoughts about relationships come to have a existence of their own
• in the physical sciences, thirdness is general theories or principles such as the Theory of Evolution, Quantum Theory, Newtonian Mechanics
• in mathematics, thirdness is a pattern or equation which explains how systems are related.  Geometrically it is represented by a triangle
• in general, it is a pattern that emerges from a series of numbers
• example:  "The Rule of Threes"

• Peirce did not think in terms of fourthness, which led him into a lot of confusing terminology such as Firstness of Second.  Some people like these complex theories, others do not
• Carl Jung stressed the quaternity, seeing it as the  minimal number for representing a unified system: a collection of overlapping, synergetic relationships.
• fourthness is a pattern which emerges from a web of relationships which support and sustain each other so that the whole is greater than the sum of the parts
• this concept is often captured by the word "synergy"
• geometrically, fourthness is the tetrahedron
• Buckminster Fuller was a creative exponent of the tetrahedron
• fourthness can also be represented as a dual network, combining hierarchy and heterarchy - this is how we think of it in Webmind
• this kind of emergence of thought is the essence of what we call "thinking" in the brain
• truly intelligent AI systems will have to be able to do this, not just follow instructions

• a philosopher that many people have found profoundly inspiring
• often thought of as a nihilist, a person preoccupied with nothingness
• he saw the "will to power" as the force which enabled people to overcome nothingness
• Neitzsche's "will to power" is equivalent to Peirce's "one law of mind".  Order emerges out of chaos because it just does.
• the universe is a huge pool of entities, struggling with each other, leading to an emerging reality

• creation myths
• Jung's observations about human nature
• modern art
• Greek mythology
• Star Wars mythology
• astrology
• Tarot cards
• other influential theories and mass media products:  Freudian psychology, MASH, etc.  See Manning's Web Site for details of how these mesh with Peircian numerical archetypes

The Mind and the Brain
• philosophers have long debated the question of the relationship between the "mind" and the brain.  Descarte in particular is associated with the idea that the mind is something quite distinct from the brain.  Not all peoples believed that the mind was in the brain;  I've heard that the ancient Egyptians did not bother to preserve the brain in mummies because they didn't see any use for it.
• in Descartes' view the rational soul was an entity distinct from the body which made contact with the body at the pineal gland.  The mind could influence the body, as well as the body influencing the mind.  He was interested in the brain and nervous system, as well as in the logic of thought, did a lot of research on reflexes and physiological psychology.
• today, complex systems theory gives us an understanding of how something as complex as the mind can emerge from the interaction of comparatively simple components.
• we will look first at some basic information about the brain and how it works, then at a model of how the mind works which is rooted in complexity theory.
How the Brain Works
• a basic issue here is "granularity" - do we look at the microscopic components - neurons and neurotransmitters - or do we look at the 100 or so major components or something in between.  The tendency has been to look at the macrosctructure or the microstructure but not so much in between
• basic cellular unit is the neuron, which is similar to nerve cells throughout the body
• connected through dendrites which are analogous to wires
• electrical signals cross a small gap between dendrites called a synapse when charge is high enough
• interaction of neurons in networks is probably more important than the nature of the individual neurons - this gets us to a higher level of granularity
• Hebb's theory:  groups of neurons called "cell assemblies" establish repeatable patterns which are the mind
• the brain is also organized into about a hundred larger units.  Over a long period of years, scientists have figured out what many of the components of the brain do. often by studying the effects of injuries to the brain or by measuring brain wave activity or by stimulating parts of the brain electrically.  Today we have the capability to scan the brain and determine what parts are most active during various activities.  It is not possible, however, to correlate the shape of the head or the size of the brain with behavioral characteristics, as phrenologists tried to do.
• we can also group these 100 major components into about 20 groups (the rows on the Whole Brain Atlas Table), or we can divide it into even larger groupings.  On the largest level of organization, we can observe that the brain is divided into two hemispheres.  Some people see this bicameral division as significant:  "left brain" (logical, analytic) vs. "right brain"  (spatial, synthetic)
• The brain can also be divided into the forebrain, the mid brain and the hind brain.  In this view of the parts of the brain:  the hind brain corresponds most closely to the brain of reptiles and is sometimes called the "reptilian brain".  It is responsible for autonomic functions, keeping the body functions going.  The mid brain is more mammalian and involved with emotion.  The forebrain is more cognitive, rational, intellectual.
• in mammals like us, the forebrain is subdivided into three parts: the hypothalamus, the thalamus and the cerebral cortex.  The cortex is divided into several parts, the largest of which are known as the cerebellum and the cerebrum, but there are also many obscurer regions like the cingulate gyri and so on.  The cortex is a gray surface layer 1 to 4 mm thick, which is divided into six layers (see diagram).  There are pyramidal neurons which link the layers, providing a form a hierarchical organization.
• the cerebellum  serves mainly to evaluate data regarding motor functions.  The cerebrum, on the other hand, is the seat of intelligence -- it's the integrative center in which complex thinking, perceiving and planning functions occur
• we are only beginning to learn about how the parts of the brain interact, although this may be the key to the mind.  Very recently published research suggests that the key to diseases such as the tremors of Parkinson's disease, severe depression, the hand wringing in obsessive compulsive disorders, and the ringing in the ears of tinnitus may be due to a decoupling of two parts of the brain that normally work in concert:  the cerebral cortex and the thalamus. 
• another surprising recent finding is that mammals are continually producing new neurons, although this was previously thought not to be the case.  This suggests there is hope for us older folks!  Newer neurons may be used for new learning.
• this research was done with macaque monkeys, who were injected with chemicals (bromodeoxyuridine) that, along with other techniques (immunohistochemistry), allowed scientists to distinguish newly created neurons.
• they then killed the monkeys (they don't mention this in their article, but how else could they get such nice slides of their brain tissue?), perfused their brains (passed solutions through slides of them, I think),  and traced the location of cells.  By observing different monkeys at different stages in the process, they could determine where the cells originated (in the syz) and where they went (to the neocortical regions).  If you log onto the Science Magazine site, you can download beautiful color graphics of their slides (click here for a sample) for $5, or read the abstract if you fill out a questionnaire.  It is one of few magazines that charges for its WEB site.
• the fact that new findings like this are released, contradicting the received wisdom in the field, highlights the fact that scientists still have a lot to learn about the brain
How the Mind Works - (according to Ben Goertzel's model)
• this is a model of "Mind" in the abstract, not just of the human mind.  The fundamental assumption is that the mind is a set of attractors of a complex system - that it results from the interaction of a large number of autonomous parts over time
• these parts can be called "agents".  They have the capacity to transform, create & destroy other agents.  Mind

consists of structures that emerge from systems of intertransforming agents.
• many of these agents act by recognizing patterns in the world, or in other agents
• thoughts, feelings and other mental entities are self-reinforcing, self-producing systems of agents
• these self-producing mental subsystems build up into a complex network of attractors and meta-attractors
• this network of subsystems & associated attractors is a "dual network" in structure; it is structured according to at least  two principles: heterarchy (associations based on similarity) and hierarchy (relationships of control between categories).
• agents pass attention ("active force") to other agents to which they are related
• because of finite memory capacity, mind must contain agents able to deal with "ungrounded" patterns, i.e. agents which were formed from now forgotten agents, or which were learned from other minds rather than at first hand -- this is    called "reasoning"
• a mind possesses agents whose goal is to recognize the mind as a whole as a pattern -- these are "self"
• the structure of the mind - the way it would look if you could take a snapshot of it at one instant, is organized according to two interacting structural archetypes - hierarchy and heterarchy
• the dynamics of the mind - the way it would look if we could see it in action in a video - follows two interacting process archetypes - evolution and autopoiesis.
• this model is too complex to "prove" mathematically, and cannot be fully tested with data about the brain.

How do chaos and complexity relate to numerical "archetypes"?

• The archetypes can be viewed as the attractors of the mind as a complex system
• Jung did not know about chaos theory, but he observed patterns which recurred in mind after mind.  These include:
• The Self - a "zero" archetype for Jung, where we begin
• The Shadow -  the "other" or "Dark Side" - evil
• Anima (female) and Animus (male), both of which are found in both men and women, and combine to form the Soul (Thirdness in Peircian terms)
• The Syzygy - Fourthness, an autopoietic system, represented for Jung by Mandalas.

• The Network Archetype - a variant of Peirce's Thirdness archetype.
• The Hierarchy Archetype - also a variant of Thirdness
• Synergy or emergence or Fourthness
• The Dual Network Archetype - this achieves Fourthness by combining the Thirdnesses of Network and Hierarchy.
• The Self - not in Jung's sense, but in the sense of an image of the whole living within the whole.  This is a fractal archetype in that the part is a recreation of the whole.  And one may find that within the self there are sub-selves, each of which recreate the whole in a different way
• Evolution and Autopoiesis - the synergy of these two archetypes working together creates an archetype of dynamics

Artificial Intelligence:  Machines which "Think"

• this raises the question of what we mean by "thinking"  Is doing calculations enough?  Playing chess?  Figuring out taxes?  These are not what we intuitively mean by "true intelligence"
• on the other hand, the term "computer" used to describe a job or profession for a human being who did computation for a living
• intelligence is not all or nothing, it is more or less.  At what point will a computer be as intelligent as we are?  Turing test?
• are chatterbots intelligent?  Try some and see what we think.

• both these approaches model part of how the human brain/mind works;  the neural net programs are modeled on the connections between neurons, the rule-based is based more on the logical capabilities of the mind.
• each is useful for some tasks, not so useful for others
• rule-based AI follows a computer design by Jon VonNeumann, a brilliant mathematician and logical theorist, the inventor of game theory
• this design has one unit with a set of logical rules, another with a lot of memory.  It goes through the following steps:
• fetch an instruction from memory.
• fetch any data required by the instruction from memory.
• execute the instruction (process the data).
• store results in memory.
• fetch another instruction from memory
• this approach is sometimes called "recursive" because it means doing the same thing over and over, taking into account the results you got the last time
• An example:  the code from the Eliza Computer Therapist program.
• a neural net is an interconnected assembly of simple processing elements, units or nodes, whose functionality is loosely based on the animal neuron. The processing ability of the network is stored in the inter-unit connection strengths, or weights, obtained by a process of adaptation to, or learning from, a set of training patterns.
• rule-based programs are good for tasks for which we know many specific rules, e.g., filling out taxes, making medical diagnoses from lists of symptoms
• neural net programs are good for tasks which we learn to do without knowing how we do them, e.g., recognizing handwriting

• Evolutionary algorithms are another approach to AI
• The basic idea here is to write programs which generate hundreds or thousands of new programs
• These programs compete with each other to accomplish goals set by the programmer
• The best programs survive, going on to build additional programs.
• Instead of just telling the computer what to do (rule-based AI) or letting it figure it out on its own (neural nets), evolutionary algorithms say "let the fittest software survive"

This mimics the evolutionary process in other realms.  Some scientist thinks that the brain itself functions through evolutionary processes.  The thoughts that do better survive, the others fall by the wayside

What will the future of AI be like?

• Many experts believe that future programs, will be more complex, combining rule-based, neural net, evolutionary and other perspectives in the same programs
• The Webmind software is one attempt to do this, combining combining insights from both the rule-based, neural net and evolutionary approaches, as well as on statistical or stochastic models
• Webmind is loosely modeled on the brain's structure, relationships between large groups of neurons, each of which is specialized
• Each of the nodes within Webmind follow rules, but the units interact with each other in ways which are analogous to how neurons in a neuronal net interact